Results 21 to 30 of about 3,865,632 (281)
Machine learning cosmological structure formation [PDF]
, 2018 We train a machine learning algorithm to learn cosmological structure formation from N-body simulations. The algorithm infers the relationship between the initial conditions and the final dark matter haloes, without the need to introduce approximate halo
Lochner, Michelle +3 more
core +2 more sources
Multilevel selection as Bayesian inference, major transitions in individuality as structure learning [PDF]
Royal Society Open Science, 2019 Complexity of life forms on the Earth has increased tremendously, primarily driven by subsequent evolutionary transitions in individuality, a mechanism in which units formerly being capable of independent replication combine to form higher-level ...
Dániel Czégel +2 more
doaj +1 more source
Bayesian Structure Learning and Sampling of Bayesian Networks with the R Package BiDAG
Journal of Statistical Software, 2023 The R package BiDAG implements Markov chain Monte Carlo (MCMC) methods for structure learning and sampling of Bayesian networks. The package includes tools to search for a maximum a posteriori (MAP) graph and to sample graphs from the posterior ...
Polina Suter +3 more
doaj +1 more source
Multiple conformations facilitate PilT function in the type IV pilus
Nature Communications, 2019 Bacterial type IV pilus-like systems catalyse the formation of pilin fibres but it is unknown how they are powered. Here, the authors present crystal and cryo-EM structures of the hexameric motor ATPases PilB and PilT from Type IVa Pilus that reveal ...
Matthew McCallum +6 more
doaj +1 more source
Hybrid Optimization Algorithm for Bayesian Network Structure Learning
Information, 2019 Since the beginning of the 21st century, research on artificial intelligence has made great progress. Bayesian networks have gradually become one of the hotspots and important achievements in artificial intelligence research.
Xingping Sun +5 more
doaj +1 more source
Learning the Structure for Structured Sparsity [PDF]
, 2015 Structured sparsity has recently emerged in statistics, machine learning and signal processing as a promising paradigm for learning in high-dimensional settings.
Bach, Francis, Shervashidze, Nino
core +6 more sources
Structure Learning via Parameter Learning [PDF]
Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, 2014 A key challenge in information and knowledge management is to automatically discover the underlying structures and patterns from large collections of extracted information. This paper presents a novel structure-learning method for a new, scalable probabilistic logic called ProPPR.
William Yang Wang +2 more
openaire +1 more source
Machine-learning mathematical structures
International Journal of Data Science in the Mathematical Sciences, 2022 We review, for a general audience, a variety of recent experiments on extracting structure from machine-learning mathematical data that have been compiled over the years. Focusing on supervised machine-learning on labeled data from different fields ranging from geometry to representation theory, from combinatorics to number theory, we present a ...
openaire +2 more sources
Journal of Statistical Software, 2017 It is well known in the literature that the problem of learning the structure of Bayesian networks is very hard to tackle: Its computational complexity is super-exponential in the number of nodes in the worst case and polynomial in most real-world ...
Marco Scutari
doaj +1 more source
A hybrid algorithm for Bayesian network structure learning with application to multi-label learning [PDF]
, 2014 We present a novel hybrid algorithm for Bayesian network structure learning, called H2PC. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges.
Aussem, Alex +2 more
core +6 more sources